ml_grid.model_classes.gaussiannb_class

Defines the GaussianNB model class.

Classes

GaussianNBWrapper	A wrapper for GaussianNB to handle integer-mapped priors for Bayesian search.
GaussianNB_class	Initializes the GaussianNB_class.

Module Contents

class ml_grid.model_classes.gaussiannb_class.GaussianNBWrapper(*, priors=None, var_smoothing=1e-09)

Bases: sklearn.naive_bayes.GaussianNB

A wrapper for GaussianNB to handle integer-mapped priors for Bayesian search.

set_params(**params: Any) → GaussianNBWrapper

Sets the parameters of the estimator.

This method intercepts the ‘priors’ parameter if it’s an integer index and maps it to the corresponding list of prior probabilities before passing it to the parent’s set_params method.

Parameters:

**params (Any) – Estimator parameters.

Returns:

The instance with updated parameters.

Return type:

GaussianNBWrapper

class ml_grid.model_classes.gaussiannb_class.GaussianNB_class(X: pandas.DataFrame | None = None, y: pandas.Series | None = None, parameter_space_size: str | None = None)

Initializes the GaussianNB_class.

Parameters:

• X (Optional[pd.DataFrame]) – Feature matrix for training. Defaults to None.

• y (Optional[pd.Series]) – Target vector for training. Defaults to None.

• parameter_space_size (Optional[str]) – Size of the parameter space for optimization. Defaults to None.

X = None

y = None

method_name = 'GaussianNB'

parameter_vector_space